Next generation automotive systems will include numerous electronic systems such as; passive safety systems for airbag deployment and anti-skid braking features; active safety for collision warning and collision avoidance features; and convenience features for Blind Spot Detection (BSD) and Adaptive Cruise Control (ACC). Today the transportation industry is rapidly moving toward solutions that support and enable these systems and new features with the ultimate goal of profitably and developing a more survivable vehicle at costs their customers are willing to pay.
These new systems will require information about the environment, targets in the environment and our relationship to them. This information will be generated from multiple sources to include data from a new class of sensor, termed non-contact sensor, and include non-contact sensors like radar, optical, laser, ultrasonic, etc. These non-contact sensors will generate data about the environment including range measurements and target classification. These systems are similar to those found on advanced tactical fighter aircraft today.
The process of manufacturing a vehicle is not much different today then is was 50 years ago; features are specified for a desired model and year; parts designed; competitively purchased; assembled; and shipped as a complete vehicle. If another feature is desired, it is integrated into the design and bolted on as a complete feature; this approach is referred to as “federated” systems. A key tenant to low cost systems in the future will be a deliberate movement away from federated features and systems, to fully integrated systems design and integrated systems.
Now with a focus on active safety systems, the sensors required to support the features identified above are to some extent common; an optical sensor that supports one optical application could be used for a second optical application; two sensors could be used for one application. In either case, simply sharing data from sensors or other sources to improve knowledge of the environment is incorrectly termed “sensor-fusion” or “multi sensor-fusion” by the transportation industry suppliers.
Those skilled in the art of state estimation, robotics, advanced defense avionics understand academically that sensor-fusion is the art of combining sensory data or data derived from disparate sources such that the resulting information is in some sense “better” than would be possible when these sources were used individually. This process is predicated on the covariance (or the measure of how much two variables vary together) of non-independent sources. The term “better” in the case above can mean more accurate, more complete, more dependable, or refer to the result of an emerging view or state estimation.
The data sources for a fusion process are not specified to originate from identical sources or sensors which may or may not be spatially and temporally aligned. Further one can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a prior knowledge about the environment and human input. Sensor fusion is also known as “multi-sensor data fusion” and is a subset of information fusion through an implementation of the probability theory.
Probability theory is the mathematical study of phenomena characterized by randomness or uncertainty. More precisely, probability is used for modeling situations when the result of a measurement, realized under the same circumstances, produces different results. Mathematicians and actuaries think of probabilities as numbers in the closed interval from 0 to 1 assigned to “events” whose occurrence or failure to occur is random. Two crucial concepts in the theory of probability are those of a random variable and of the probability distribution of a random variable.
Implementing the features described above with affordable instruments requires reliable real-time estimates of system state. Unfortunately, the complete state is not always observable. State Estimation takes all the data obtained and uses it to determine the underlying behavior of the system at any point in time. It includes fault detection, isolation and continuous system state estimation.
There are two parts to state estimation: modeling and algorithms. The overall approach is to use a model to predict the behavior of the system in a particular state, and then compare that behavior with the actual measurements from the instruments to determine which state or states is the most likely to produce the observed system behavior.
This is not well understood or currently implemented in the transportation industry; the approach understood and practiced is logical decisions in linear and deterministic systems. If use cases require higher confidences in measurements, instrument specifications are tightened resulting in the undesired effect of cost and schedule increases. The environment we live and operate in is neither linear nor deterministic; use cases are infinite; and the perverse variability of the targets and potential maneuvers cannot be modeled. The variability of the problem identified above includes aspects other than just spatial (i.e. range and bearing to a target); temporal relationships are part of the fundamental intellectual structure (together with space and number) within which events must be sequenced, quantify the duration of events, quantify the intervals between them, and compare the kinematics of objects.
The automotive industry today implements features in such a way that all aspects are contained within the system (federated) and therefore reasonably controlled. Sharing information like target reports with other features is anticipated and desired; however measurements reported with respect to the integrated system will be historical in nature and received asynchronously. Timing errors can induce more error in the system than a bad measurement. These and other issues can be addressed with the introduction of a suite of modeling tools based on re-thinking the approach of federated systems and focusing on an integrated systems solution based on state estimation.
Central to the successful implementation of the advanced safety systems discussed above is ensuring ability for the system to cope with and recover from emergency situations. If one or more emergency conditions arise, systems of the future must quickly initiate and successfully execute procedures to mitigate the condition and then recover; these procedures must be performed under tight timing constraints, e.g., pre-crash systems.
This patent describes a system and methods necessary to implement a design methodology that will facilitate and support advanced safety systems design, test, verification, and validation; with emphasis on reliable fault tolerant operation. The implementation of this system and methods is termed “Algorithm Prototyping Analysis and Test” or APAT.
As it stands today, there is much art published documenting the research and development in the area of procedure analysis and design. However, there are no systematic and rigorous methodologies for designing procedures to be used in advanced safety systems for the transportation industry. This is currently viewed as a serious shortcoming, since these high-risk and complex systems employ procedures and action sequences that can and do impact life or death results.
In the future it will be the responsibility of the onboard computers to automatically initiate and execute procedures and recovery sequences. Therefore modeling, analysis, verification, and design of these highly critical methods, procedures and recovery sequences are required and are thus the focus of this patent.
In a discussion of Prior Art, U.S. Pat. No. 7,006,950, Statistical Modeling and Performance Characterization of a Real-time Dual Camera Surveillance System, Greiffenhagen, et al.; the invention relates to a method for visually detecting and tracking an object through a space. The method derives statistical models for errors, including quantifying an indexing step performed by an indexing module, and tuning system parameters. Further the method applies a likelihood model for candidate hypothesis evaluation and object parameters estimation for locating the object. This invention relates specifically to computer vision systems, more particularly to a system having computationally efficient real-time object detection, tracking, and zooming capabilities. The need arises for methods of modeling more than 1 data source against features that require an infinitely variable combination of instruments and measurements.
In a discussion of Prior Art, U.S. Pat. No. 6,028,548, Vehicle Collision Radar with Randomized FSK Waveform, Farmer; describes an automotive radar for improved immunity to jamming from other automotive radars utilizing common modeling methods such as Auto Regressive Modeling (ARMA) and Minimum Variance Spectral Estimation which are just two such methods that would be applicable. It is recognized in the art that modeling sequences implemented as tools or embedded systems will yield desired results. As with U.S. Pat. No. 7,006,950, the modeling sequences identified are based on the improvement of federated devices.
In a discussion of Prior Art, U.S. Pat. No. 7,079,993, Automated Generator of Optimal Models for the Statistical Analysis of Data, Stephenson, et al; describes an automated process for producing accurate statistical models from sample data tables designed to solve for optimal parameters of each statistical model considered, computes test statistics and degrees of freedom in the model, and uses these test statistics and degrees of freedom to establish a complete ordering of the statistical models. Stephenson further describes how the process arrives at a statistical model that is highest in the ordering and is thus deemed most suitable to model the sample data table. These techniques are basically described in the general art of data or information fusion and modeling. It would not be obvious that general and well published statistical techniques should be applied to the art of automotive safety systems design.
Therefore, a need exists for a system and method for modeling sensor system inputs in a synthetic environment against truth tables to achieve optimal performance and cost parameters for the design and implementation of advanced automotive safety systems.